In order to assure good quality control, many present day electronic assemblies are tested at one or more stages during the manufacturing process. Such measurements are generally conducted by measuring one or more electrical parameters of the electronic assembly by means of automated testing machines.
Such parametric measurements are usually associated with some amount of electrical "noise" due to contact resistance in the test probes, temperature variations, component variations and other factors. For each parameter, the noise manifests itself by causing repetitive measurements of the parameter to yield different results even if the same device is used for all of the tests.
The spread in the resulting values often results in difficulty and errors in determining whether a particular measured value of a parameter falls within predetermined acceptable ranges. For example, an actual parameter value may fall outside of the acceptable range, but, due to noise, the measured value of that parameter may be inside the acceptable range, thereby causing the tested device to be erroneously accepted. Alternatively, an actual parameter value may fall within the acceptable range, but, due to noise the measured value of that parameter may be outside of the acceptable range, thereby causing the tested device to be erroneously rejected.
Consequently, when noise is a problem, it is common practice to take a number of measurements of a particular parameter, average the results and compare the average to the acceptable range. However, if, as is often the case, the average is close to the acceptance or rejection limits, the number of measurements used to generate the average may be increased in order to insure that predetermined quality control limits are met. A great amount of time can be consumed by multiple measurements. As a result the test portion of the manufacturing process can seriously reduce the overall throughput of the process. In addition, the decision to increase or decrease the number of measurements used to generate the average or to accept or reject units with averages near the limits may be left to operator discretion causing unpredictable results.
An additional problem may arise because the "true" value of the measurement is not known due to the noise. Consequently, prior art methods have been developed to estimate a true parameter value from multiple measurements taken of that parameter.
One of the aforementioned prior art methods is simple averaging in which the results of a predetermined number of measurements of the test parameter are averaged to generate an estimate of the true parameter value. During subsequent testing, a fixed number of measurements is performed on a new device, the measurement values are averaged, the average value is compared to the pass/fail limit and a decision is made to accept or reject the device. The primary assumption with the averaging method is that the average of a large number of equally-weighted measurement values is an accurate approximation of the true test parameter value. The method is only efficient where parameter values are very tightly grouped near the pass/fail limit compared to the width of the measurement error; otherwise many more measurements are taken than are required to establish a good estimate of the measurement value. Unfortunately, in most parameter measurement situations, the parameter values do not meet the latter criteria and many more measurements are made than are necessary in most cases, consequently, the simple averaging method is inefficient and results in relatively high test costs to insure a predetermined accuracy.
Other prior art statistical methods have been developed which can be used to estimate the number of repetitive measurements that must be taken in order to insure, with a predetermined probability, that a particular quality control level is reached. A prior art method which can estimate the number of repetitive measurements that must be taken is called the "Bayes" method and is described in more detail in an article entitled "A Bayesian Approach to Improving Test Effectiveness and Evaluating Test Strategy", G. Larsen and R. M. Mahoney, Proceedings of the ATE West Conference, Anaheim, Calif. (January 1990). The Bayes method has significant advantages over simple averaging because it needs only pass/fail decision from each test. Consequently, in many cases, it is the only statistical method which can be used where only pass/fail information is available (such as certain pass/fail medical diagnostic tests).
As described in the aforementioned article, the Bayes method is an iterative process. Certain inputs relating to the measurement process must be known from either a theoretical analysis of the measurement process or from production history and these values are used to compute a statistical pass/fail limit number. During a subsequent actual measurement sequence, a measured value is used compute a new statistical number and this latter number is compared to the previously computed limit number to determine whether the particular device under test (DUT) passes or fails or whether another measurement should be taken. Repetitive measurements are taken until "resolution" is achieved: the DUT either passes or fails.
One problem with the Bayes method is that it requires three particular inputs: expected failure rate, false alarm rate, and missed fault rate in order to compute the required statistical numbers. If there has been a significant production history for a testable parameter and the production processes are stable, the expected failure rate can be easily calculated by well-known statistical methods (although for tests conducted at very "basic" levels, such as in-circuit tests conducted component-by-component, the actual measurement variations may far exceed that predicted by statistical computations).
The false alarm rate and the missed fault rate are typically not well-known even after significant production experience. In addition, the performance predicted by the Bayes method is only achievable if the aforementioned rates are specifically known for each particular device and parameter being tested, for example, for a particular voltage reading on a given unit. When just a historical average rate across multiple DUTs is used, the performance is drastically lower than that predicted. Since the historical average is generally the only parameter that is available, the Bayes algorithm is not as efficient as predicted.
Consequently, it is an object of the present invention to provide a statistical testing method which efficiently evaluates parameter measurements which are subject to noise.
It is another object of the present invention to provide a statistical testing method in which preset target error rates can be attained with fewer average test iterations than prior art methods.
It is still another object of the present invention to provide a statistical testing method that provides explicit control over target error rates.
It is still another object of the present invention to provide a statistical testing method which provides control over the maximum test iterations.
It is yet another object of the present invention to provide a statistical testing method which can be used with both a single pass/fail limit and dual pass/fail limits.
It is a further object of the present invention to provide a statistical testing method in which a special "marginal" category can be defined which can be used to categorize certain DUTs and improve overall performance of the testing method.
It is yet a further object of the present invention to provide a statistical testing method in which the statistical test which is used to control test procedure can be selected to optimize performance of the method on both production testing and tests performed on a single DUT.